library(readxl)
Rubber <- read_excel("C:/Users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx")
View(Rubber)
# Importing Data
CAMBODIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "k1:k325")
# Checking the Imported Data
View(CAMBODIA)
# Creating Time Series Data
CAMBODIA_ts <- ts(CAMBODIA, start=c(2005,1), end=c(2016,12), frequency=12)
# Viewing and Checking the Created Time Series Data
CAMBODIA_ts
sum(is.na(CAMBODIA_ts))
library(forecast)
CAMBODIA_ts <- tsclean(CAMBODIA_ts)
CAMBODIA_ts
# Identification: Plotting the Time Series Data
plot(CAMBODIA_ts)
# Estimating the appropriate model
CAMBODIA_ts_model <- auto.arima(CAMBODIA_ts)
CAMBODIA_ts_model
# Forecasting
options(max.print=1000000)
CAMBODIA_ts_forecast <- forecast (CAMBODIA_ts_model, level=c(95), h=288)
plot(CAMBODIA_ts_forecast)
CAMBODIA_ts_forecast
# Exporting
write.table(CAMBODIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/CAMBODIA_TSA.csv", sep=",")
# Importing Data
GAUTIMALA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "c1:c325")
# Checking the Imported Data
View(GAUTIMALA)
# Creating Time Series Data
GAUTIMALA_ts <- ts(GAUTIMALA, start=c(2004,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
GAUTIMALA_ts
sum(is.na(GAUTIMALA_ts))
library(forecast)
GAUTIMALA_ts <- tsclean(GAUTIMALA_ts)
GAUTIMALA_ts
# Identification: Plotting the Time Series Data
plot(GAUTIMALA_ts)
# Estimating the appropriate model
GAUTIMALA_ts_model <- auto.arima(GAUTIMALA_ts)
GAUTIMALA_ts_model
# Forecasting
options(max.print=1000000)
GAUTIMALA_ts_forecast <- forecast (GAUTIMALA_ts_model, level=c(95), h=276)
plot(GAUTIMALA_ts_forecast)
GAUTIMALA_ts_forecast
# Exporting
write.table(GAUTIMALA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/GAUTIMALA_TSA.csv", sep=",")
# Importing Data
NIGERIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "c1:c325")
# Checking the Imported Data
View(NIGERIA)
# Importing Data
NIGERIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "g1:g325")
# Checking the Imported Data
View(NIGERIA)
# Creating Time Series Data
NIGERIA_ts <- ts(NIGERIA, start=c(1991,1), end=c(2014,12), frequency=12)
# Viewing and Checking the Created Time Series Data
NIGERIA_ts
sum(is.na(NIGERIA_ts))
# Importing Data
NIGERIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "g1:g325")
# Checking the Imported Data
View(NIGERIA)
# Creating Time Series Data
NIGERIA_ts <- ts(NIGERIA, start=c(1991,1), end=c(2014,12), frequency=12)
# Viewing and Checking the Created Time Series Data
NIGERIA_ts
sum(is.na(NIGERIA_ts))
library(forecast)
NIGERIA_ts <- tsclean(NIGERIA_ts)
NIGERIA_ts
# Identification: Plotting the Time Series Data
plot(NIGERIA_ts)
# Estimating the appropriate model
NIGERIA_ts_model <- auto.arima(NIGERIA_ts)
NIGERIA_ts_model
# Forecasting
options(max.print=1000000)
NIGERIA_ts_forecast <- forecast (NIGERIA_ts_model, level=c(95), h=312)
plot(NIGERIA_ts_forecast)
NIGERIA_ts_forecast
# Exporting
write.table(NIGERIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/NIGERIA_TSA.csv", sep=",")
# Importing Data
CHINA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "b1:b325")
# Checking the Imported Data
View(CHINA)
# Creating Time Series Data
CHINA_ts <- ts(CHINA, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
CHINA_ts
sum(is.na(CHINA_ts))
library(forecast)
CHINA_ts <- tsclean(CHINA_ts)
CHINA_ts
# Identification: Plotting the Time Series Data
plot(CHINA_ts)
# Estimating the appropriate model
CHINA_ts_model <- auto.arima(CHINA_ts)
CHINA_ts_model
# Forecasting
options(max.print=1000000)
CHINA_ts_forecast <- forecast (CHINA_ts_model, level=c(95), h=276)
plot(CHINA_ts_forecast)
CHINA_ts_forecast
# Exporting
write.table(CHINA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/CHINA_TSA.csv", sep=",")
# Importing Data
INDIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "d1:d325")
# Checking the Imported Data
View(INDIA)
# Creating Time Series Data
INDIA_ts <- ts(INDIA, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
INDIA_ts
sum(is.na(INDIA_ts))
library(forecast)
INDIA_ts <- tsclean(INDIA_ts)
INDIA_ts
# Identification: Plotting the Time Series Data
plot(INDIA_ts)
# Estimating the appropriate model
INDIA_ts_model <- auto.arima(INDIA_ts)
INDIA_ts_model
# Forecasting
options(max.print=1000000)
INDIA_ts_forecast <- forecast (INDIA_ts_model, level=c(95), h=276)
plot(INDIA_ts_forecast)
INDIA_ts_forecast
# Exporting
write.table(INDIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/INDIA_TSA.csv", sep=",")
# Importing Data
INDONESIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "e1:e325")
# Checking the Imported Data
View(INDONESIA)
# Creating Time Series Data
INDONESIA_ts <- ts(INDONESIA, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
INDONESIA_ts
sum(is.na(INDONESIA_ts))
library(forecast)
INDONESIA_ts <- tsclean(INDONESIA_ts)
INDONESIA_ts
# Identification: Plotting the Time Series Data
plot(INDONESIA_ts)
# Estimating the appropriate model
INDONESIA_ts_model <- auto.arima(INDONESIA_ts)
INDONESIA_ts_model
# Forecasting
options(max.print=1000000)
INDONESIA_ts_forecast <- forecast (INDONESIA_ts_model, level=c(95), h=276)
plot(INDONESIA_ts_forecast)
INDONESIA_ts_forecast
# Exporting
write.table(INDONESIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/INDONESIA_TSA.csv", sep=",")
# Importing Data
MALAYSIA<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "f1:f325")
# Checking the Imported Data
View(MALAYSIA)
# Creating Time Series Data
MALAYSIA_ts <- ts(MALAYSIA, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
MALAYSIA_ts
sum(is.na(MALAYSIA_ts))
library(forecast)
MALAYSIA_ts <- tsclean(MALAYSIA_ts)
MALAYSIA_ts
# Identification: Plotting the Time Series Data
plot(MALAYSIA_ts)
# Estimating the appropriate model
MALAYSIA_ts_model <- auto.arima(MALAYSIA_ts)
MALAYSIA_ts_model
# Forecasting
options(max.print=1000000)
MALAYSIA_ts_forecast <- forecast (MALAYSIA_ts_model, level=c(95), h=276)
plot(MALAYSIA_ts_forecast)
MALAYSIA_ts_forecast
# Exporting
write.table(MALAYSIA_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/MALAYSIA_TSA.csv", sep=",")
# Importing Data
PHILIPINESS<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "h1:h325")
# Checking the Imported Data
View(PHILIPINESS)
# Creating Time Series Data
PHILIPINESS_ts <- ts(PHILIPINESS, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
PHILIPINESS_ts
sum(is.na(PHILIPINESS_ts))
library(forecast)
PHILIPINESS_ts <- tsclean(PHILIPINESS_ts)
PHILIPINESS_ts
# Identification: Plotting the Time Series Data
plot(PHILIPINESS_ts)
# Estimating the appropriate model
PHILIPINESS_ts_model <- auto.arima(PHILIPINESS_ts)
PHILIPINESS_ts_model
# Forecasting
options(max.print=1000000)
PHILIPINESS_ts_forecast <- forecast (PHILIPINESS_ts_model, level=c(95), h=276)
plot(PHILIPINESS_ts_forecast)
PHILIPINESS_ts_forecast
# Exporting
write.table(PHILIPINESS_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/PHILIPINESS_TSA.csv", sep=",")
# Importing Data
THAILAND<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "i1:i325")
# Checking the Imported Data
View(THAILAND)
# Creating Time Series Data
THAILAND_ts <- ts(THAILAND, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
THAILAND_ts
sum(is.na(THAILAND_ts))
library(forecast)
THAILAND_ts <- tsclean(THAILAND_ts)
THAILAND_ts
# Identification: Plotting the Time Series Data
plot(THAILAND_ts)
# Estimating the appropriate model
THAILAND_ts_model <- auto.arima(THAILAND_ts)
THAILAND_ts_model
# Forecasting
options(max.print=1000000)
THAILAND_ts_forecast <- forecast (THAILAND_ts_model, level=c(95), h=276)
plot(THAILAND_ts_forecast)
THAILAND_ts_forecast
# Exporting
write.table(THAILAND_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/THAILAND_TSA.csv", sep=",")
# Importing Data
VIETNAM<- read_excel("C:/users/Biniam/Desktop/Documents/Academic/Thesis/Analysis Folder/Excel Files/Rubber/Rubber.xlsx",sheet = "Sheet1", range = "j1:j325")
# Checking the Imported Data
View(VIETNAM)
# Creating Time Series Data
VIETNAM_ts <- ts(VIETNAM, start=c(1991,1), end=c(2017,12), frequency=12)
# Viewing and Checking the Created Time Series Data
VIETNAM_ts
sum(is.na(VIETNAM_ts))
library(forecast)
VIETNAM_ts <- tsclean(VIETNAM_ts)
VIETNAM_ts
# Identification: Plotting the Time Series Data
plot(VIETNAM_ts)
# Estimating the appropriate model
VIETNAM_ts_model <- auto.arima(VIETNAM_ts)
VIETNAM_ts_model
# Forecasting
options(max.print=1000000)
VIETNAM_ts_forecast <- forecast (VIETNAM_ts_model, level=c(95), h=276)
plot(VIETNAM_ts_forecast)
VIETNAM_ts_forecast
# Exporting
write.table(VIETNAM_ts_forecast, file="/users/Biniam/Desktop/Documents/Academic/Thesis/Result Folder/TSA Results/Excel Files/From R/Rubber/VIETNAM_TSA.csv", sep=",")
